A Systematic Literature Review on Personalised Learning in the Higher Education Context

Rida Indah Fariani, Kasiyah Junus, Harry Budi Santoso

Research output: Contribution to journalArticlepeer-review

9 Citations (Scopus)

Abstract

Personalised learning (PL) is learning in which the stage of learning and the instructional approach are optimised for the needs of each learner. The concept of PL allows e-learning design to shift from a ‘one size fits all’ approach to an adaptive and student-centred approach. This paper aims to provide a literature review of PL based on: the PL components used to analyse learner diversity, the PL features offered, the methods used in developing the PL model, the resulting model, the learning theories applied and the impact of PL implementation. Thirty-nine out of 1654 articles published between 2017 and 2021 which were found by Kitchenham method were studied and analysed. The results are derived from synthesized through qualitative synthesis using thematic analysis. The results reveal that most of the articles used knowledge level and learner characteristics to analyse learner diversity. The teaching materials and learning path were the most widely offered PL features in PL model. There is a trend in determining PL features using the knowledge graph method and the use of machine learning classification algorithms to analyse learner diversity. The results also show that PL implementation improves learning outcomes and increases learner’s satisfaction, motivation, and engagement. Research analysing the impact of PL implementation on learning is limited. In addition, only a few studies explicitly referred to learning theory in relation to PL model development. Further research topics are suggested.

Original languageEnglish
Pages (from-to)449-476
Number of pages28
JournalTechnology, Knowledge and Learning
Volume28
Issue number2
DOIs
Publication statusAccepted/In press - 2022

Keywords

  • Adaptive learning
  • Learning strategy
  • Personalised component learning
  • Personalised learning
  • Personalised model

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